Current crowdsourcing platforms provide little support for worker feedback. Workers are sometimes invited to post free text describing their experience and preferences in completing tasks. They can also use forums such as Turker Nation1 to exchange preferences on tasks and requesters. In fact, crowdsourcing platforms rely heavily on observing workers and inferring their preferences implicitly. On the contrary, we believe that asking workers to indicate their preferences explicitly will allow us to improve different processes in crowdsourcing platforms. We initiate a study that leverages explicit elicitation from workers to capture the evolving nature of worker preferences and we propose an optimization framework to better understand and estimate task completion time. We design a worker model to estimate task completion time whose accuracy is improved iteratively by requesting worker preferences for task factors, such as, required skills, task payment, and task relevance. We develop efficient solutions with guarantees, run extensive experiments with large scale real world data that show the benefit of explicit preference elicitation over implicit ones with statistical significance.