Retinal vessel segmentation plays an important role in the early diagnosis and monitoring of many ocular and systemic diseases. However, labeled medical imaging data is scarce, costly, and requires pixel-level precision, making few-shot learning a promising solution to this challenge. This paper presents a novel threshold-based co-training framework using dual network for few-shot retinal vessel segmentation. Specifically, the method employs two segmentation models initialized with different parameters, which collaboratively learn by leveraging high-confidence pseudo-labels generated through a thresholding mechanism. The proposed method balances segmentation accuracy and robustness by integrating binary cross-entropy and Dice loss, effectively minimizing noise and uncertainty. Evaluation on the CHASE_DB1 dataset shows superior performance compared to state-of-the-art methods, achieving improvements in accuracy, sensitivity, specificity, and Dice score. These findings highlight the potential of threshold-based co-trained dual network for efficient and accurate retinal vessel segmentation using limited data.