Traditional federated learning relies on fully labeled datasets in each medical institution, which is impractical in real-world clinical scenarios. Federated Active Learning (FAL) addresses this by selecting a few informative samples for labeling, but it faces challenges such as domain shift across institutions. Besides, existing FAL methods rely on single-round model knowledge to estimate prediction-level uncertainty, ignoring uncertainty from features and model evolution during training. In this work, we propose TM-FAL, a novel framework for federated active medical image classification under domain shift. TM-FAL proposes a new uncertainty by integrating feature differences and prediction confidence from temporal local and global models to capture both local-global differences and the inherent complexity of images. Additionally, we use the prediction of the global model as pseudo labels to group images to mitigate class imbalance caused by uncertainty-based selection. Experiments on two medical image classification datasets demonstrate that TM-FAL outperforms various state-of-the-art methods.