diff --git a/doc/source/whatsnew/v1.5.0.rst b/doc/source/whatsnew/v1.5.0.rst
index 1ae76984484af..6dee1de31dd7e 100644
--- a/doc/source/whatsnew/v1.5.0.rst
+++ b/doc/source/whatsnew/v1.5.0.rst
@@ -257,6 +257,7 @@ Sparse
 ExtensionArray
 ^^^^^^^^^^^^^^
 - Bug in :meth:`IntegerArray.searchsorted` and :meth:`FloatingArray.searchsorted` returning inconsistent results when acting on ``np.nan`` (:issue:`45255`)
+- Bug in :class:`Series` construction with index and empty data when :class:`ExtensionDtype` has ``na_value`` of None (:issue:`44602`)
 -
 
 Styler
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 4e69596539bed..a92f0333098bb 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -74,7 +74,6 @@
     is_list_like,
     is_object_dtype,
     is_scalar,
-    pandas_dtype,
     validate_all_hashable,
 )
 from pandas.core.dtypes.generic import ABCDataFrame
@@ -100,6 +99,7 @@
 from pandas.core.arrays.sparse import SparseAccessor
 import pandas.core.common as com
 from pandas.core.construction import (
+    construct_1d_arraylike_from_scalar,
     create_series_with_explicit_dtype,
     extract_array,
     is_empty_data,
@@ -495,7 +495,9 @@ def _init_dict(
         elif index is not None:
             # fastpath for Series(data=None). Just use broadcasting a scalar
             # instead of reindexing.
-            values = na_value_for_dtype(pandas_dtype(dtype), compat=False)
+            values = na_value_for_dtype(dtype, compat=False)
+            if values is None:
+                values = construct_1d_arraylike_from_scalar(values, len(index), dtype)
             keys = index
         else:
             keys, values = (), []
diff --git a/pandas/tests/extension/test_common.py b/pandas/tests/extension/test_common.py
index e43650c291200..b5da0b0eab9ce 100644
--- a/pandas/tests/extension/test_common.py
+++ b/pandas/tests/extension/test_common.py
@@ -1,3 +1,6 @@
+from collections import abc
+import numbers
+
 import numpy as np
 import pytest
 
@@ -9,30 +12,81 @@
 from pandas.core.arrays import ExtensionArray
 
 
-class DummyDtype(dtypes.ExtensionDtype):
+class DummyClass:
     pass
 
 
+class DummyDtype(dtypes.ExtensionDtype):
+
+    type = DummyClass
+    name = "dummy"
+
+    @classmethod
+    def construct_from_string(cls, string):
+        if string == cls.name:
+            return cls()
+        else:
+            raise TypeError(f"Cannot construct a '{cls}' from '{string}'")
+
+    @classmethod
+    def construct_array_type(cls):
+        return DummyArray
+
+
 class DummyArray(ExtensionArray):
+
+    _dtype = DummyDtype
+
     def __init__(self, data):
-        self.data = data
+        self.data = np.array(data)
 
-    def __array__(self, dtype):
+    def __array__(self, dtype=None):
         return self.data
 
     @property
     def dtype(self):
-        return DummyDtype()
+        return DummyArray._dtype()
 
     def astype(self, dtype, copy=True):
         # we don't support anything but a single dtype
-        if isinstance(dtype, DummyDtype):
+        if isinstance(dtype, self._dtype):
             if copy:
                 return type(self)(self.data)
             return self
 
         return np.array(self, dtype=dtype, copy=copy)
 
+    def __len__(self):
+        return len(self.data)
+
+    @classmethod
+    def _from_sequence(cls, scalars, dtype=None, copy=False):
+        if isinstance(scalars, cls._dtype.type):
+            scalars = [scalars]
+        return DummyArray(scalars)
+
+    def take(self, indices, allow_fill=False, fill_value=None):
+        from pandas.core.algorithms import take
+
+        data = self.astype(object)
+
+        if allow_fill and fill_value is None:
+            fill_value = self.dtype.na_value
+
+        result = take(data, indices, fill_value=fill_value, allow_fill=allow_fill)
+        return self._from_sequence(result, dtype=self.dtype)
+
+    def isna(self):
+        return np.array([x is self.dtype.na_value for x in self.data], dtype="bool")
+
+    def __getitem__(self, idx):
+        if isinstance(idx, numbers.Integral):
+            return self.data[idx]
+        elif isinstance(idx, (abc.Iterable, slice)):
+            return DummyArray(self.data[idx])
+        else:
+            raise TypeError("Index type not supported", idx)
+
 
 class TestExtensionArrayDtype:
     @pytest.mark.parametrize(
@@ -79,3 +133,11 @@ def test_astype_no_copy():
 def test_is_extension_array_dtype(dtype):
     assert isinstance(dtype, dtypes.ExtensionDtype)
     assert is_extension_array_dtype(dtype)
+
+
+@pytest.mark.parametrize("na_value", [np.nan, pd.NA, None])
+def test_empty_series_construction(monkeypatch, na_value):
+    monkeypatch.setattr(DummyDtype, "na_value", na_value)
+    result = pd.Series(index=[1, 2, 3], dtype=DummyDtype())
+    expected = pd.Series([na_value] * 3, index=[1, 2, 3], dtype=DummyDtype())
+    tm.assert_series_equal(result, expected)