  Semi-supervised medical image segmentation aims to leverage limited annotated
data and rich unlabeled data to perform accurate segmentation. However,
existing semi-supervised methods are highly dependent on the quality of
self-generated pseudo labels, which are prone to incorrect supervision and
confirmation bias. Meanwhile, they are insufficient in capturing the label
distributions in latent space and suffer from limited generalization to
unlabeled data. To address these issues, we propose a Latent Diffusion Label
Rectification Model (DiffRect) for semi-supervised medical image segmentation.
DiffRect first utilizes a Label Context Calibration Module (LCC) to calibrate
the biased relationship between classes by learning the category-wise
correlation in pseudo labels, then apply Latent Feature Rectification Module
(LFR) on the latent space to formulate and align the pseudo label distributions
of different levels via latent diffusion. It utilizes a denoising network to
learn the coarse to fine and fine to precise consecutive distribution
transportations. We evaluate DiffRect on three public datasets: ACDC, MS-CMRSEG
2019, and Decathlon Prostate. Experimental results demonstrate the
effectiveness of DiffRect, e.g. it achieves 82.40\% Dice score on ACDC with
only 1\% labeled scan available, outperforms the previous state-of-the-art by
4.60\% in Dice, and even rivals fully supervised performance. Code is released
at \url{https://github.com/CUHK-AIM-Group/DiffRect}.
