Semi-supervised learning leverages limited pixel-level annotated data and abundant unlabeled data to achieve effective semantic image segmentation. To address this, we propose a semi-supervised learning framework, integrated with a bidirectional bisect copy-paste (B2P) mechanism. We introduce a B2CP strategy applied to labeled and unlabeled data in the second teacher network, preserving both data types to enhance training diversity. This mechanism, coupled with copy-paste-based supervision for the student network, effectively mitigates interference from uncontrollable regions. Extensive experiments on the ACDC public datasets demonstrate the efficiency of the proposed model. It surpasses the fully supervised U-Net at a 5% labeled data and 20% labeled data.