Semi-Supervised Learning (SSL) enhances medical image segmentation by leveraging unlabeled data to alleviate the scarcity of annotated samples. Although vision foundation models (e.g., the Segment Anything Model, SAM) demonstrate strong generalization and feature extraction capabilities, their integration with SSL remains underexplored. To address this, we propose a novel dual-branch SSL-SAM framework that enables iterative pseudo-label refinement through a co-training mechanism, harnessing labeled and unlabeled data. For enhanced segmentation precision, we introduce Cross-Path Prompt Fusion (CPF), which facilitates dynamic interaction between prompt-based segmentation paths, enhancing feature representation. Additionally, we develop Frequency Enhanced Pseudo-label Filtering (FEPF), a denoising strategy that suppresses high-frequency noise while preserving anatomical boundaries, improving pseudo-label reliability. Comprehensive experiments on four medical image datasets demonstrate that our approach consistently outperforms existing SSL and SAM-based methods, particularly in data-scarce scenarios. Code is available at: https://github.com/Heirudy/Dual-SAM .