Current successful choroidal vessel segmentation methods rely on large amounts of voxel-level annotations on the 3D optical coherence tomography images, which are hard and time-consuming. Semi-supervised learning solves this issue by enabling model learning from both unlabeled data and a limited amount of labeled data. A challenge is the defective pseudo labels generated for the unlabeled data. In this work, we propose a model-based label-to-image diffusion (MLD) framework for semi-supervised choroidal vessel segmentation. We first generate pseudo labels from unlabeled images with a coarse correspondence using a model-based strategy. Then, we generate precisely corresponding images of pseudo labels by a hierarchical diffusion probabilistic model. We evaluated our method on myopia data with a new topological connectivity metric. The quantitative and qualitative experimental results indicate the effectiveness of the label-to-image diffusion framework and its benefit for enhancing the existing supervised choroidal segmentation methods. The code is available at: https://github.com/nicetomeetu21/MLD.