The human eye gaze is an important non-verbal cue that can unobtrusively provide information about the intention and attention of a user to enable intelligent interactive systems. Eye gaze can also be taken as input to systems as a replacement of the conventional mouse and keyboard, and can also be indicative of the cognitive state of the user. However, estimating and applying gaze in real-world applications poses significant challenges. In this chapter, we first review the development of gaze estimation methods in recent years. We especially focus on learning-based gaze estimation methods which benefit from large-scale data and deep learning methods that recently became available. Second, we discuss the challenges of using gaze estimation for real-world applications and our efforts toward making these methods easily usable for the Human-Computer Interaction community. At last, we provide two application examples, demonstrating the use of eye gaze to enable attentive and adaptive interfaces.