医学图像分割是许多临床方法中的基础性且关键性步骤。半监督学习已广泛应用于医学图像分割任务中，因为它减轻了获取经过专家审查的标注的沉重负担并利用了更容易获得的未标记数据的优势。虽然已证明一致性学习通过强制对不同分布下的预测进行不变性可是一种有效的方法，但现有方法不能充分利用来自未标记数据的区域级形状约束和边界级距离信息。本文提出了一种新颖的基于不确定性引导的互相一致性学习框架，通过集成来自最新预测的任务内一致性学习和来自任务层面正则化的任务间一致性学习以利用几何形状信息，从而有效地利用未标记数据。该框架基于模型的估计分割不确定性来选择相对确定的预测进行一致性学习，从而有效地利用未标记数据中更可靠的信息。在两个公开可用的基准数据集上的实验表明：（1）我们提出的方法可以通过利用未标记数据实现显著的性能提升，在左心房分割和脑肿瘤分割上与监督基线相比，Dice系数分别提高了4.13％和9.82％；（2）与其他半监督分割方法相比，我们的方法在相同的骨干网络和任务设置下在两个数据集上都取得了更好的分割效果，展示了我们的方法的有效性和稳健性以及其他医学图像分割任务的潜在可转移性。版权所有© 2022 Elsevier B.V. 发表。

Medical image segmentation is a fundamental and critical step in many clinical approaches. Semi-supervised learning has been widely applied to medical image segmentation tasks since it alleviates the heavy burden of acquiring expert-examined annotations and takes the advantage of unlabeled data which is much easier to acquire. Although consistency learning has been proven to be an effective approach by enforcing an invariance of predictions under different distributions, existing approaches cannot make full use of region-level shape constraint and boundary-level distance information from unlabeled data. In this paper, we propose a novel uncertainty-guided mutual consistency learning framework to effectively exploit unlabeled data by integrating intra-task consistency learning from up-to-date predictions for self-ensembling and cross-task consistency learning from task-level regularization to exploit geometric shape information. The framework is guided by the estimated segmentation uncertainty of models to select out relatively certain predictions for consistency learning, so as to effectively exploit more reliable information from unlabeled data. Experiments on two publicly available benchmark datasets showed that: (1) Our proposed method can achieve significant performance improvement by leveraging unlabeled data, with up to 4.13% and 9.82% in Dice coefficient compared to supervised baseline on left atrium segmentation and brain tumor segmentation, respectively. (2) Compared with other semi-supervised segmentation methods, our proposed method achieve better segmentation performance under the same backbone network and task settings on both datasets, demonstrating the effectiveness and robustness of our method and potential transferability for other medical image segmentation tasks.Copyright © 2022. Published by Elsevier B.V.