When crowdsourced workers perform annotation tasks in an unfamiliar domain, their accuracy will dramatically decline due to the lack of expertise. Transferring knowledge from relevant domains can form a better representation for instances, which benefits the estimation of workers' expertise in truth inference models. However, existing knowledge transfer processes for crowdsourcing require a considerable number of well-collected instances in source domains. This paper proposes a novel truth inference model for crowdsourcing, where (meta-)knowledge is transferred by meta-learning and used in the estimation of workers' expertise. Our preliminary experiments demonstrate that the meta-knowledge transfer significantly reduces instances in source domains and increases the accuracy of truth inference.