Semi-supervised medical image segmentation (SSMIS) has been demonstrated the potential to mitigate the issue of limited medical labeled data. However, confirmation and cognitive biases may affect the prevalent teacher-student based SSMIS methods due to erroneous pseudo-labels. To tackle this challenge, we improve the mean teacher approach and propose the Students Discrepancy-Informed Correction Learning (SDCL) framework that includes two students and one non-trainable teacher, which utilizes the segmentation difference between the two students to guide the self-correcting learning. The essence of SDCL is to identify the areas of segmentation discrepancy as the potential bias areas, and then encourage the model to review the correct cognition and rectify their own biases in these areas. To facilitate the bias correction learning with continuous review and rectification, two correction loss functions are employed to minimize the correct segmentation voxel distance and maximize the erroneous segmentation voxel entropy. We conducted experiments on three public medical image datasets: two 3D datasets (CT and MRI) and one 2D dataset (MRI). The results show that our SDCL surpasses the current State-of-the-Art (SOTA) methods by 2.57\%, 3.04\%, and 2.34\% in the Dice score on the Pancreas, LA, and ACDC datasets, respectively. In addition, the accuracy of our method is very close to the fully supervised method on the ACDC dataset, and even exceeds the fully supervised method on the Pancreas and LA dataset. (Code available at \url{https://github.com/pascalcpp/SDCL}).

翻译：半监督医学图像分割（SSMIS）已被证明具有缓解医学标注数据有限问题的潜力。然而，由于伪标签错误，确认偏差和认知偏差可能影响当前主流的基于师生框架的SSMIS方法。为应对这一挑战，我们改进了平均教师方法，提出了基于学生差异感知的修正学习（SDCL）框架。该框架包含两个学生网络和一个不可训练的教师网络，利用两个学生网络之间的分割差异来指导自我修正学习。SDCL的核心思想是将分割差异区域识别为潜在的偏差区域，进而鼓励模型在这些区域中重新审视正确认知并修正自身偏差。为促进具有持续审视与修正特性的偏差修正学习，我们采用了两种修正损失函数：最小化正确分割体素距离和最大化错误分割体素熵。我们在三个公共医学图像数据集上进行了实验：两个3D数据集（CT和MRI）和一个2D数据集（MRI）。结果表明，我们的SDCL方法在胰腺、左心房和心脏数据集上的Dice分数分别比当前最先进（SOTA）方法提高了2.57%、3.04%和2.34%。此外，我们的方法在心脏数据集上的准确率已非常接近全监督方法，在胰腺和左心房数据集上甚至超越了全监督方法。（代码公开于 \url{https://github.com/pascalcpp/SDCL}）。