Aiming at the problem that traditional deep learning strategies used in steel plate surface defect images classification rely on abundant labeled samples. This paper proposes an efficient deep active learning method with a lightweight convolutional neural network and a novel uncertainty based active learning strategy. The network adopts a simplified convolutional base to do feature extraction, and replaces the hidden layer in the final densely connected classifier with global pooling layer to mitigate overfitting. To better measure model uncertainty about unlabeled image samples, this method first passes unlabeled images through the model trained by labeled image samples to obtain the probability distribution over classes ( PDC) for every unlabeled sample, then uses the same model to make predictions on the labeled samples to get an average PDC for every class. The KL-divergence value between these two kinds of distributions can be used as a new uncertainty measure to select unlabeled images for annotation. According to the experiments on NEUCLS dataset, the proposed method can reach 97% accuracy with 44% labeled data, which can reduce annotation cost greatly.