Eye movement includes the voluntary or involuntary movement of the eyes. The eye tracker device is used for measuring the eye movements. Eye gaze data analytics can bring out information about observer's age, sex, education, experience, visual processing, cognitive processing, and many more. The thesis attempts to use the fact that the person's experience and expertise have an impact on their eye gaze pattern. The experts' eye gaze pattern while viewing the medical images depicts the attentional behaviour of the individual, which has been captured and further utilised for detecting the target region. The research work strives to automate and propose a complete eye gaze-based system that uses attentional theories inferred from eye gaze pattern. The designed system is evaluated for detecting optic disc in fundus retinal image. The chosen topic has been interesting as optic disc detection is a fundamental task for retinal image processing for classifying other fundus structures and is crucial for the identification of eye-related diseases. Owing to the fact that the human visual perception has been less studied for optic disc detection and an attempt to derive eye gaze based data analytics, the thesis first discovers how human perception works for optic disc detection using bottomup visual attention model. The inference/ conclusions derived, paved way to propose an eye gaze-based optic disc detection (EGODD) system to detect optic disc in fundus retinal images. The eye gaze data while the user is performing simple target search task were collected from different users groups comprising of expert and nonexpert groups. Extensive data analysis has been carried to extract eye gaze features like fixation and using machine approach label regions in fundus retinal image. The segregated labelled data has been used to build a top down knowledge to bias the search map towards the target region.