Choroid structure is crucial for the diagnosis of ocular diseases, and deep supervised learning (SL) techniques have been widely applied to segment the choroidal structure based on OCT images. However, SL requires massive annotated data, which is difficult to obtain. Researchers have explored semi-supervised learning (SSL) methods based on consistency regularization and achieved strong performance. However, these methods suffer from heavy computational burdens and introduce noise that hinders the training process. To address these issues, we propose a thickness distribution prior and uncertainty aware pseudo-label selection SSL framework (Prior-SSL) for OCT choroidal segmentation. Specifically, we compute the instance-level uncertainty of the pseudo-label candidate, which significantly reduces the computational burden of uncertainty estimation. In addition, we consider the physiological characteristics of the choroid, explore the choroidal thickness distribution as prior knowledge in the pseudo-label selection procedure, and thereby obtain more reliable and accurate pseudo-labels. Finally, these two branches are combined via a Modified AND-Gate (MAG) to assign confidence levels to pseudo-label candidates. We achieve state-of-the-art performance for the choroidal segmentation task on the GOALS and NIDEK OCT datasets. Ablation studies verify the effectiveness of the Prior-SSL in selecting high-quality pseudo-labels.