Intensive competition between network operators as well as steady increase in mobile traffic call for additional investments into the networking infrastructure. Keeping current mobile networks profitable, the following criteria should be satisfied: end-user quality expectations need to be fulfilled on the one hand and service quality overprovisioning should be eliminated on the other. This generates growing demand for adequate QoE estimation models accounting for dominant mobile data services. Moreover, novel models have to fulfill requirements of good applicability and high accuracy. Our approach in this paper details an advanced QoE estimation model, extensively verified with appropriate statistical tools, for the most popular mobile web services: browsing, download, and upload. The proposed model follows from a recent extensive QoE assessment and utilizes the bitrate together with the initial loading delay as well-measurable input parameters. Targeted to estimate the mean opinion score, our proposed model demonstrates an excellent convergence across the considered practical scenarios.