  Semi-supervised medical image segmentation offers a promising solution for
large-scale medical image analysis by significantly reducing the annotation
burden while achieving comparable performance. Employing this method exhibits a
high degree of potential for optimizing the segmentation process and increasing
its feasibility in clinical settings during translational investigations.
Recently, cross-supervised training based on different co-training sub-networks
has become a standard paradigm for this task. Still, the critical issues of
sub-network disagreement and label-noise suppression require further attention
and progress in cross-supervised training. This paper proposes a
cross-supervised learning framework based on dual classifiers (DC-Net),
including an evidential classifier and a vanilla classifier. The two
classifiers exhibit complementary characteristics, enabling them to handle
disagreement effectively and generate more robust and accurate pseudo-labels
for unlabeled data. We also incorporate the uncertainty estimation from the
evidential classifier into cross-supervised training to alleviate the negative
effect of the error supervision signal. The extensive experiments on LA and
Pancreas-CT dataset illustrate that DC-Net outperforms other state-of-the-art
methods for semi-supervised segmentation. The code will be released soon.
