Microtasking involves breaking down a job into smaller tasks and assigning them to a group of workers who voluntarily complete the tasks and receive payment. However, the joint estimation of quality and determination of payment remains an underexplored area in microtask crowdsourcing research. This paper addresses the limitation of unestimated payment on microtasking platforms by introducing a dynamic worker quality and payment estimation algorithm known as Dynamic Quality Payment (DQP). Utilizing the Bee Colony Optimization (BCO) algorithm, the proposed approach integrates worker quality estimation and payment determination. DQP incorporates the Gaussian Process Model (GPM) to initially estimate worker quality and then optimize payments based on the quality of work. Empirical testing of the algorithm is conducted using two datasets from Amazon Mechanical Turk. Additionally, the DQP algorithm is compared against novel microtasking and quality estimation algorithms, demonstrating superior performance. The experimental results illustrate that DQP not only reduces the cost of microtasking but also accurately estimates worker quality.