Medical image segmentation heavily depends on labeled data, but its acquisition is costly and time-consuming. Semi-supervised learning (SSL) utilizes unlabeled data to relieve this burden, yet existing methods suffer from inaccurate pseudo-labels caused by distribution mismatch, hard label information loss, and inter-class size imbalance. To address these issues, we propose an advanced semi-supervised framework for medical image segmentation (ISSD-NET). ISSD-NET incorporates three key components: First, the Grad-CAM Splicing Module (GCSM) leverages Grad-CAM to locate semantically rich regions and splice them into augmented samples, aligning labeled and unlabeled data distributions for more reliable pseudo-labels. Second, the Self-Distillation Consistency Regularization Module (SCRM) generates soft pseudo-labels as supplementary supervision, preserving uncertainty and fine-grained details to overcome hard label limitations. Additionally, the Adaptive q-vMF Dice Loss dynamically adjusts similarity metrics to address class imbalance, further refining pseudo-label quality. Experiments on ACDC and LA datasets show that ISSD-NET outperforms state-of-the-art methods in semi-supervised medical segmentation, enhancing accuracy and offering a robust solution for reliable medical image analysis.