Recent advancement in deep learning has led to an increased interest in image processing and computer vision applications for driver monitoring systems. One of the applications where these techniques can be useful is in segmenting and tracking seatbelts. A seatbelt is an important safety feature in the vehicle that if properly used can save lives. Efficient segmentation of the seatbelts in an image provides important information about the correct use of seatbelts. The challenge in developing deep learning algorithms for seatbelt detection and segmentation is the manual annotations required for this task, which is cumbersome. This paper explores a novel formulation to efficiently train a seatbelt model with minimal supervision. We exploit the textureless and shape characteristics of the seatbelts to programmatically synthesize images. Our proposed method synthetically creates images that resemble seatbelt patterns. After training a model exclusively with synthetic images, we iteratively fine-tune it using naturalistic images extracted from online video-sharing websites. The labels for these images are pseudo-labels assigned by the model to confident predictions. Fine-tuning helps adapt the model to better work on real naturalistic images, improving the performance of the system. We obtain an F1-score of 0.55 in segmenting the seatbelt with this approach. We also experiment with fine-tuning the model with a small number of naturalistic images with annotated labels. After pretraining on synthetic samples and pseudo-labeled naturalistic images, we achieve an F1-score of 0.67 using only 200 annotated images.