High-resolution (HR) magnetic resonance imaging (MRI) offers exceptional visualization of human tissue but is often limited by hardware constraints. While recent super-resolution (SR) methods leveraging learned codebooks have shown promise, they often overlook the rich anatomical priors inherent in MRI data. To address this, we propose a probabilistic prior-guided anatomical alignment for MRI super-resolution (PGASR) method that incorporates anatomical knowledge into the SR process. Specifically, we first introduce an anatomical-conditioned codebook generation (ACG) module that generates rough anatomical structure maps by extracting the regions of interest from MRI slices. These maps are used as anatomical conditions for the discrete codebook generation. Then, to better exploit information between MRI slices, we propose a prior matching alignment (PMA) module that aligns the codebook index matching probabilities between adjacent slices, as well as across low-resolution (LR) and high-resolution (HR) domains, thereby reducing the loss of image details. We validate the effectiveness of the proposed PGASR method with the public MRI dataset IXI. The experimental results demonstrate that PGASR outperforms state-of-the-art methods.