The optic disc (OD) is the point where the retinal vessels begin. OD carries essential information linked to Diabetic Retinopathy and glaucoma that may cause vision loss. Therefore, accurate segmentation of the optic disc from eye fundus images is essential to develop efficient automated DR and glaucoma detection systems. This paper presents a deep learning-based system for OD segmentation based on an ensemble of efficient semantic segmentation models for medical image segmentation. The aggregation of the different DL models was performed with the ordered weighted averaging (OWA) operators. We proposed the use of a dynamically generated set of weights that can give a different contribution to the models according to their performance during the segmentation of OD in the eye fundus images. The effectiveness of the proposed system was assessed on a fundus image dataset collected from the Hospital Sant Joan de Reus. We obtained Jaccard, Dice, Precision, and Recall scores of 95.40, 95.10, 96.70, and 93.90%, respectively.