diff --git a/xarray/namedarray/core.py b/xarray/namedarray/core.py
index 2fef1cad3db..d3fcffcfd9e 100644
--- a/xarray/namedarray/core.py
+++ b/xarray/namedarray/core.py
@@ -40,7 +40,6 @@
 
     from xarray.core.types import Dims
     from xarray.namedarray._typing import (
-        DuckArray,
         _AttrsLike,
         _Chunks,
         _Dim,
@@ -144,9 +143,9 @@ def _new(
 @overload
 def from_array(
     dims: _DimsLike,
-    data: DuckArray[_ScalarType],
+    data: duckarray[_ShapeType, _DType],
     attrs: _AttrsLike = ...,
-) -> _NamedArray[_ScalarType]:
+) -> NamedArray[_ShapeType, _DType]:
     ...
 
 
@@ -155,15 +154,15 @@ def from_array(
     dims: _DimsLike,
     data: ArrayLike,
     attrs: _AttrsLike = ...,
-) -> _NamedArray[Any]:
+) -> NamedArray[Any, Any]:
     ...
 
 
 def from_array(
     dims: _DimsLike,
-    data: DuckArray[_ScalarType] | ArrayLike,
+    data: duckarray[_ShapeType, _DType] | ArrayLike,
     attrs: _AttrsLike = None,
-) -> _NamedArray[_ScalarType] | _NamedArray[Any]:
+) -> NamedArray[_ShapeType, _DType] | NamedArray[Any, Any]:
     """
     Create a Named array from an array-like object.
 
@@ -184,7 +183,7 @@ def from_array(
             "Array is already a Named array. Use 'data.data' to retrieve the data array"
         )
 
-    # TODO: dask.array.ma.masked_array also exists, better way?
+    # TODO: dask.array.ma.MaskedArray also exists, better way?
     if isinstance(data, np.ma.MaskedArray):
         mask = np.ma.getmaskarray(data)  # type: ignore[no-untyped-call]
         if mask.any():
diff --git a/xarray/tests/test_namedarray.py b/xarray/tests/test_namedarray.py
index 448e8cf819a..e0141e12755 100644
--- a/xarray/tests/test_namedarray.py
+++ b/xarray/tests/test_namedarray.py
@@ -290,10 +290,14 @@ def test_duck_array_typevar(a: duckarray[Any, _DType]) -> duckarray[Any, _DType]
 
     numpy_a: NDArray[np.int64]
     numpy_a = np.array([2.1, 4], dtype=np.dtype(np.int64))
+    test_duck_array_typevar(numpy_a)
+
+    masked_a: np.ma.MaskedArray[Any, np.dtype[np.int64]]
+    masked_a = np.ma.asarray([2.1, 4], dtype=np.dtype(np.int64))  # type: ignore[no-untyped-call]
+    test_duck_array_typevar(masked_a)
+
     custom_a: CustomArrayIndexable[Any, np.dtype[np.int64]]
     custom_a = CustomArrayIndexable(numpy_a)
-
-    test_duck_array_typevar(numpy_a)
     test_duck_array_typevar(custom_a)
 
     # Test numpy's array api: