  Semi-supervised learning (SSL) has shown considerable potential in medical
image segmentation, primarily leveraging consistency regularization and
pseudo-labeling. However, many SSL approaches only pay attention to low-level
consistency and overlook the significance of pseudo-label reliability.
Therefore, in this work, we propose an adversarial self-training consistency
framework (AstMatch). Firstly, we design an adversarial consistency
regularization (ACR) approach to enhance knowledge transfer and strengthen
prediction consistency under varying perturbation intensities. Second, we apply
a feature matching loss for adversarial training to incorporate high-level
consistency regularization. Additionally, we present the pyramid channel
attention (PCA) and efficient channel and spatial attention (ECSA) modules to
improve the discriminator's performance. Finally, we propose an adaptive
self-training (AST) approach to ensure the pseudo-labels' quality. The proposed
AstMatch has been extensively evaluated with cutting-edge SSL methods on three
public-available datasets. The experimental results under different labeled
ratios indicate that AstMatch outperforms other existing methods, achieving new
state-of-the-art performance. Our code will be available at
https://github.com/GuanghaoZhu663/AstMatch.
