Title: Curriculum-inspired Training for Selective Neural Networks. Abstract: We consider the problem of training neural network models for selective classification, where the models have the reject option to abstain from predicting certain examples as needed. Recent advances in curriculum learning  have demonstrated the benefit of leveraging the  example difficulty scores in training deep neural networks for typical classification settings. Example difficulty scores are even more important in selective classification as a lower prediction error rate can be achieved by rejecting hard examples and accepting easy ones. In this paper, we propose a curriculum-inspired method to train selective neural network models by leveraging example difficulty scores. Our method tailors the curriculum idea to selective neural network training by calibrating the ratio of easy and hard examples in each mini-batch, and exploiting difficulty ordering at the mini-batch level. Our experimental results demonstrate that our method outperforms both the state-of-the-art and alternative methods using vanilla curriculum techniques for training selective neural network models.