Consistency regularization and pseudo labeling-based semi-supervised methods perform co-training using the pseudo labels from multi-view inputs. However, such co-training models tend to converge early to a consensus, degenerating to the self-training ones, and produce low-confidence pseudo labels from the perturbed inputs during training. To address these issues, we propose an Uncertainty-guided Collaborative Mean-Teacher (UCMT) for semi-supervised semantic segmentation with the high-confidence pseudo labels. Concretely, UCMT consists of two main components: 1) collaborative mean-teacher (CMT) for encouraging model disagreement and performing co-training between the sub-networks, and 2) uncertainty-guided region mix (UMIX) for manipulating the input images according to the uncertainty maps of CMT and facilitating CMT to produce high-confidence pseudo labels. Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation. Extensive experiments on four public medical image datasets including 2D and 3D modalities demonstrate the superiority of UCMT over the state-of-the-art. Code is available at: https://github.com/Senyh/UCMT.

翻译：一致性正则化和基于伪标签的半监督方法利用多视角输入产生的伪标签进行协同训练。然而，这类协同训练模型容易过早收敛至一致状态，退化为自训练方法，且在训练过程中从扰动输入中生成低置信伪标签。为解决这些问题，我们提出了一种面向高置信伪标签的不确定性引导协同均值教师（UCMT）模型用于半监督语义分割。具体而言，UCMT包含两个主要组件：1）协同均值教师（CMT）通过鼓励子网络间的模型分歧实现协同训练；2）不确定性引导区域混合（UMIX）机制根据CMT的不确定性图谱操纵输入图像，促进CMT生成高置信伪标签。通过融合UMIX与CMT的优势，UCMT能有效保持模型分歧并提升协同训练中伪标签质量。在包含二维和三维模态的四个公开医学图像数据集上的大量实验表明，UCMT优于现有最先进方法。代码见：https://github.com/Senyh/UCMT。