Polarization-sensitive (PS-OCT) optical coherence tomography provides valuable insight information of tissue birefringence by analyzing the sensitive light scattering. Current PS-OCT systems require intricate optical alignment and an additional spectrometer, making them complex and resource-intensive. To address these challenges, we propose a novel approach that leverages contrastive unpaired translation (CUT) to synthesize PS-OCT images directly from standard OCT intensity data. In our study, we applied the patch-based CUT model to generate cross-sectional PS-OCT images. A dataset was created to capture the tendon healing process in mice over six weeks, resulting in 10,000 paired B-scan samples of PS-OCT and OCT images. These were divided into training and testing datasets with a 4:1 ratio for all GAN models. Our findings demonstrate that CUT is an effective deep learning method for generating high-quality synthetic PS-OCT images. Validation through classification models showed that up to 90% of the synthetic samples meet the same structure as PSOCT B-scans provided from SD-PS-OCT system. This work highlights CUT as a reliable and efficient solution for creating synthetic PS-OCT images, supported by consistent classification and repeatability results.