In this work, we consider semi-supervised segmentation as a dense prediction problem using prototype vector correlation and propose a simple way to represent each segmentation class with multiple prototype vectors. To avoid degenerate solutions, two regularization strategies are applied on unlabeled images, based on mutual information maximization and orthogonality. The first one ensures that all prototype vectors are considered by the network, while the other one explicitly enforces prototypes to be orthogonal by decreasing their cosine distance. Experimental results on two benchmark medical segmentation datasets reveal our method's effectiveness in improving segmentation performance when few annotated images are available.