diff --git a/doc/source/whatsnew/v2.0.0.rst b/doc/source/whatsnew/v2.0.0.rst
index 12b0d90e68ab9..f6e0b5bc0cf36 100644
--- a/doc/source/whatsnew/v2.0.0.rst
+++ b/doc/source/whatsnew/v2.0.0.rst
@@ -853,6 +853,7 @@ Indexing
 - Bug in :meth:`DataFrame.loc` coercing dtypes when setting values with a list indexer (:issue:`49159`)
 - Bug in :meth:`Series.loc` raising error for out of bounds end of slice indexer (:issue:`50161`)
 - Bug in :meth:`DataFrame.loc` raising ``ValueError`` with ``bool`` indexer and :class:`MultiIndex` (:issue:`47687`)
+- Bug in :meth:`DataFrame.loc` raising ``IndexError`` when setting values for a pyarrow-backed column with a non-scalar indexer (:issue:`50085`)
 - Bug in :meth:`DataFrame.__setitem__` raising ``ValueError`` when right hand side is :class:`DataFrame` with :class:`MultiIndex` columns (:issue:`49121`)
 - Bug in :meth:`DataFrame.reindex` casting dtype to ``object`` when :class:`DataFrame` has single extension array column when re-indexing ``columns`` and ``index`` (:issue:`48190`)
 - Bug in :meth:`DataFrame.iloc` raising ``IndexError`` when indexer is a :class:`Series` with numeric extension array dtype (:issue:`49521`)
diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 7e954b3d1d1ec..f5ff4f4b2dc7c 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -951,7 +951,7 @@ def pyarrow_meth(data, skip_nulls, **kwargs):
             return self.dtype.na_value
         return result.as_py()
 
-    def __setitem__(self, key: int | slice | np.ndarray, value: Any) -> None:
+    def __setitem__(self, key, value) -> None:
         """Set one or more values inplace.
 
         Parameters
@@ -972,6 +972,10 @@ def __setitem__(self, key: int | slice | np.ndarray, value: Any) -> None:
         -------
         None
         """
+        # GH50085: unwrap 1D indexers
+        if isinstance(key, tuple) and len(key) == 1:
+            key = key[0]
+
         key = check_array_indexer(self, key)
         value = self._maybe_convert_setitem_value(value)
 
@@ -997,7 +1001,6 @@ def __setitem__(self, key: int | slice | np.ndarray, value: Any) -> None:
                 return
 
         indices = self._indexing_key_to_indices(key)
-
         argsort = np.argsort(indices)
         indices = indices[argsort]
 
diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index 6954c97007d23..422b9effeface 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -348,7 +348,7 @@ def __getitem__(
         """
         raise AbstractMethodError(self)
 
-    def __setitem__(self, key: int | slice | np.ndarray, value: Any) -> None:
+    def __setitem__(self, key, value) -> None:
         """
         Set one or more values inplace.
 
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 63940741c3fe3..50069b6e65cfa 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -407,7 +407,7 @@ def _get_getitem_freq(self, key) -> BaseOffset | None:
     # error: Argument 1 of "__setitem__" is incompatible with supertype
     # "ExtensionArray"; supertype defines the argument type as "Union[int,
     # ndarray]"
-    def __setitem__(  # type: ignore[override]
+    def __setitem__(
         self,
         key: int | Sequence[int] | Sequence[bool] | slice,
         value: NaTType | Any | Sequence[Any],
diff --git a/pandas/tests/extension/base/setitem.py b/pandas/tests/extension/base/setitem.py
index f52abb9349578..73445a96f4a03 100644
--- a/pandas/tests/extension/base/setitem.py
+++ b/pandas/tests/extension/base/setitem.py
@@ -418,3 +418,11 @@ def test_setitem_invalid(self, data, invalid_scalar):
 
         with pytest.raises((ValueError, TypeError), match=msg):
             data[:] = invalid_scalar
+
+    def test_setitem_2d_values(self, data):
+        # GH50085
+        original = data.copy()
+        df = pd.DataFrame({"a": data, "b": data})
+        df.loc[[0, 1], :] = df.loc[[1, 0], :].values
+        assert (df.loc[0, :] == original[1]).all()
+        assert (df.loc[1, :] == original[0]).all()