From medical research to gaming applications, gaze estimation is becoming a valuable tool. While there exists a number of hardware-based solutions, recent deep learning-based approaches, coupled with the availability of large-scale databases, have allowed to provide a precise gaze estimate using only consumer sensors. However, there remains a number of questions, regarding the problem formulation, architectural choices and learning paradigms for designing gaze estimation systems in order to bridge the gap between geometry-based systems involving specific hardware and approaches using consumer sensors only. In this paper, we introduce a deep, end-to-end trainable ensemble of heatmap-based weak predictors for 2D/3D gaze estimation. We show that, through heterogeneous architectural design of these weak predictors, we can improve the decorrelation between the latter predictors to design more robust deep ensemble models. Furthermore, we propose a stochastic combinatory loss that consists in randomly sampling combinations of weak predictors at train time. This allows to train better individual weak predictors, with lower correlation between them. This, in turns, allows to significantly enhance the performance of the deep ensemble. We show that our Deep heterogeneous ensemble with Stochastic Combinatory loss (DeeSCo) outperforms state-of-the-art approaches for 2D/3D gaze estimation on multiple datasets.