  Segmentation of pathological images is a crucial step for accurate cancer
diagnosis. However, acquiring dense annotations of such images for training is
labor-intensive and time-consuming. To address this issue, Semi-Supervised
Learning (SSL) has the potential for reducing the annotation cost, but it is
challenged by a large number of unlabeled training images. In this paper, we
propose a novel SSL method based on Cross Distillation of Multiple Attentions
(CDMA) to effectively leverage unlabeled images. Firstly, we propose a
Multi-attention Tri-branch Network (MTNet) that consists of an encoder and a
three-branch decoder, with each branch using a different attention mechanism
that calibrates features in different aspects to generate diverse outputs.
Secondly, we introduce Cross Decoder Knowledge Distillation (CDKD) between the
three decoder branches, allowing them to learn from each other's soft labels to
mitigate the negative impact of incorrect pseudo labels in training.
Additionally, uncertainty minimization is applied to the average prediction of
the three branches, which further regularizes predictions on unlabeled images
and encourages inter-branch consistency. Our proposed CDMA was compared with
eight state-of-the-art SSL methods on the public DigestPath dataset, and the
experimental results showed that our method outperforms the other approaches
under different annotation ratios. The code is available at
\href{https://github.com/HiLab-git/CDMA}{https://github.com/HiLab-git/CDMA.}
