Although recent advancements in fully supervised learning have led to notable successes in medical image segmentation, acquiring high-quality pixel-wise annotations from experts in the medical imaging domain continues to pose a significant challenge. Therefore, semi-supervised learning (SSL) is receiving increasing attention in medical image segmentation. Most existing semi-supervised learning (SSL) algorithms typically apply varying levels of perturbations to the student and teacher models, utilizing Exponential Moving Average (EMA) for parameter transfer. However, these methods are limited by the constraints of homogeneous networks, which can lead to the problem of model cognitive bias, restricting further exploration of the performance of dual-network models. We propose a novel Multi-task Heterogeneous Framework (MHF) aimed at correcting network biases and enhancing the performance of semi-supervised medical image segmentation. The MHF introduces two different sub-networks, each performing predictions for different tasks, to explore the potential of heterogeneous networks. Specifically, we use two heterogeneous sub-networks for different task predictions: one sub-net for segmentation prediction and the other for level set function prediction. In addition, we blend pseudo-labels from various modalities based on the Dice scores of the sub-nets. The merged pseudo-labels are then utilized to compute the unsupervised loss. Experimental results on the CT medical image dataset demonstrate that our method outperforms several state-of-the-art methods.