Deep neural networks (DNNs) have been a key technique for click-through rate (CTR) estimation, yet existing DNNs-based CTR models neglect the inconsistency between their optimization objectives (e.g., Binary Cross Entropy, BCE) and CTR ranking metrics (e.g., Area Under the ROC Curve, AUC). It is noteworthy that directly optimizing AUC by gradient-descent methods is difficult due to the non-differentiable Heaviside function built-in AUC. To this end, we propose a smooth approximation of AUC, called smooth-AUC (SAUC), towards the rank-based CTR prediction. Specifically, SAUC relaxes the Heaviside function via sigmoid with a temperature coefficient (aiming at controlling the function sharpness) in order to facilitate the gradient-based optimization. Furthermore, SAUC is a plug-and-play objective that can be used in any DNNs-based CTR model. Experimental results on two real-world datasets demonstrate that SAUC consistently improves the recommendation accuracy of current DNNs-based CTR models.