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.

翻译：近年来，不确定性感知方法在半监督医学图像分割领域引起了广泛关注。然而，现有方法通常存在难以在同一框架内平衡计算成本、估计精度与理论支撑的缺陷。为解决此问题，我们将登普斯特-沙弗证据理论（DST）引入半监督医学图像分割，提出了证据推理学习（EVIL）。EVIL提供了理论保障的解决方案，能够在单次前向传播中推断出精确的不确定性量化。在完成不确定性估计后，可为未标注数据生成可信的伪标签。我们采用近期提出的基于一致性正则化的训练范式，通过强制扰动预测间的一致性，在少量标注数据条件下增强模型泛化能力。实验结果表明，在公开数据集上，EVIL与多种现有先进方法相比具有竞争力的性能表现。