Recently, uncertainty-aware methods have attracted increasing attention in
semi-supervised medical image segmentation. However, current methods usually
suffer from the drawback that it is difficult to balance the computational
cost, estimation accuracy, and theoretical support in a unified framework. To
alleviate this problem, we introduce the Dempster-Shafer Theory of Evidence
(DST) into semi-supervised medical image segmentation, dubbed Evidential
Inference Learning (EVIL). EVIL provides a theoretically guaranteed solution to
infer accurate uncertainty quantification in a single forward pass. Trustworthy
pseudo labels on unlabeled data are generated after uncertainty estimation. The
recently proposed consistency regularization-based training paradigm is adopted
in our framework, which enforces the consistency on the perturbed predictions
to enhance the generalization with few labeled data. Experimental results show
that EVIL achieves competitive performance in comparison with several
state-of-the-art methods on the public dataset.

通过将证据理论引入半监督医学图像分割，以在单次前向传递中推理准确的不确定性量化为目的，提出了命名为 EVIL 的确定性推理学习方法。该方法使用一致性正则化训练范式，通过对扰动预测的一致性进行约束来增强少量标记数据的泛化能力。实验结果表明，EVIL 在公共数据集上与几种最先进的方法相比取得了竞争性的性能。