Efficient intravascular access in trauma and critical care significantly impacts patient outcomes. However, the availability of skilled medical personnel in austere environments is often limited. Despite advances in autonomous needle insertion, inaccuracies in vessel segmentation predictions pose risks. Understanding the uncertainty of predictive models in ultrasound imaging is crucial for assessing their reliability. We introduce MSU-Net, a novel multistage approach for training an ensemble of U-Nets to yield ultrasound image segmentation maps. We demonstrate substantial improvements, 27.7% over a single Monte Carlo U-Net, enhancing uncertainty evaluations, model transparency, and trustworthiness. By identifying areas where the model is highly confident, MSU-Net helps to better interpret anatomical details and improve the understanding of vessel locations.