  Classification and segmentation are crucial in medical image analysis as they
enable accurate diagnosis and disease monitoring. However, current methods
often prioritize the mutual learning features and shared model parameters,
while neglecting the reliability of features and performances. In this paper,
we propose a novel Uncertainty-informed Mutual Learning (UML) framework for
reliable and interpretable medical image analysis. Our UML introduces
reliability to joint classification and segmentation tasks, leveraging mutual
learning with uncertainty to improve performance. To achieve this, we first use
evidential deep learning to provide image-level and pixel-wise confidences.
Then, an Uncertainty Navigator Decoder is constructed for better using mutual
features and generating segmentation results. Besides, an Uncertainty
Instructor is proposed to screen reliable masks for classification. Overall,
UML could produce confidence estimation in features and performance for each
link (classification and segmentation). The experiments on the public datasets
demonstrate that our UML outperforms existing methods in terms of both accuracy
and robustness. Our UML has the potential to explore the development of more
reliable and explainable medical image analysis models. We will release the
codes for reproduction after acceptance.
