Automatic multi-organ segmentation in abdominal computed tomography (CT) scans is crucial for accurate computer-aided diagnosis. Nowadays, numerous semi-supervised learning (SSL) techniques have been introduced to leverage the vast amount of unlabeled data. However, the class imbalanced issue still impedes accurate segmentation, particularly for small organs in multi-organ segmentation. To address this issue, we propose a class-aware cross pseudo supervision (C \(^2\) PS) framework, which built upon cross pseudo supervision (CPS) method. Specifically, Our approach enhances network learning for small organs in unlabeled data through a dynamic threshold-based consistency (DTC) loss, while a dedicated organ-specific weighted (OSW) loss is designed for labeled data. We make full use of the label distributions of each organ and the pseudo-label distributions of each organ output by the model in order to direct the model’s attention towards smaller organs. Results on public benchmarks show that our method outperforms competing SSL techniques, as demonstrated by improved mean Dice (2.36% to 3.44%) and mean Jaccard (2.67% - 3.51%) on the FLARE2022 dataset, mean Dice(0.93% - 1.81%) and mean Jaccard (0.99% to 2.13%) on the AMOS2022 dataset.